The Future of AI is Personalization — and Service Organizations Have Everything to Gain

The Future of AI is Personalization — and Service Organizations Have Everything to Gain

AI can improve productivity and customer experience through predictive tools and automation. But as leaders plan to increase AI use across every part of the organization, it’s essential to understand the different types of AI and how to choose the right tools to help personalize the service industry. 

According to Twilio’s State of Personalization 2023 report, 69% of businesses are increasing their investment in personalization. And the service industry is no exception, especially since the end goal is to deliver seamless service experiences to customers. This means interactions that are quick, efficient, accurate, and contain the least amount of touchpoints possible. 

Using AI developed for service, organizations can make good on that goal and deliver memorable experiences that their customers expect and prefer. Read on to learn what AI-driven personalization is, what’s next for personalization in vertical AI solutions like Aquant’s Service Co-Pilot—and how service organizations like yours can get the most value out of this ever-evolving tool.

What is AI-driven personalization?

Simply put, AI-powered personalization involves collecting and analyzing customer data within generative AI models. In the case of service organizations, these data types can range from free text to machine documentation to intel from subject matter experts (and beyond!). When such data is plugged into a generative AI platform and analyzed, the outputs yield insights, patterns, styles, and correlations that provide tailored experiences for current—and future—customers. 

Generative AI can be split into two categories: horizontal and vertical. Horizontal AI tools, like ChatGPT and Google Bard, boast generalized capabilities. But vertical solutions, like Service Co-Pilot, address industry-specific challenges. 

Whether you’re looking to meet rising CX standards, fill labor gaps, or upskill workers quickly, vertical solutions for service help diagnose and resolve issues faster and more accurately than ever. They use data to offer accurate self-service options, allocate resources, and minimize downtime through proactive maintenance. And once you add the personalized element, you get the efficiency and experience that today’s customers expect.

The benefits of personalizing your service delivery

Meaningful customer experiences are vital to a service organization’s success. Customers want to feel valued and cared for—personalization is integral to providing that. Some benefits of personalizing your service delivery include: 

  • Enhancing CX and improving customer satisfaction: Personalized interactions indicate that the business is attentive to customers’ preferences, history, and habits. McKinsey’s Next in Personalization report revealed that 71% of customers expect personalization and 76% get frustrated when they don’t experience it. 
  • Increased customer loyalty and retention: Customers that feel understood are more likely to purchase again. These positive, repeat interactions foster trust and reduce churn (and could result in referrals!).
  • More upselling and cross-selling opportunities: Service orgs that understand customer purchasing history/behavior can recommend other offerings that align with buyer interests, which increases potential revenue. 
  • Gaining valuable customer insights: Customer data holds valuable insights about preferences, behavior patterns, and trends. These findings can help frame strategic decision-making, product development, and marketing efforts—all of which help service orgs stay ahead of the competition. 


How AI for service is becoming more personalized

We can expect service AI tools to embrace personalization in the following ways:

  • Improving data collection by adopting multi-modal fusion and hybrid approaches: Generative AI is about structure and context. You can use generative AI to integrate data from multiple sources within your service org, such as miscellaneous technician notes, product documentation, subject matter experts, and existing service data. This creates a solid foundation that you can use to establish KPIs, develop data models, and understand your customers and their habits. Additionally, service AI combines your in-house data with aggregate knowledge, widening its scope and providing better outputs.
  • Providing context: Besides improving how it collects and analyzes user data, service organizations can use the outputs to gain context into customers’ preferences, behaviors, patterns, and more. Additionally, you can use AI personalization with your workforce. For example, let’s say two technicians have the same amount of experience in the field. Technician A is better at solving mechanical issues while Technician B is great at fixing electrical problems. Personalized generative AI can help you assign major tasks that suit a technician’s core skill set, or you could use it to upskill your technicians. 
  • Integrating user feedback to adapt and learn: True AI and machine learning cannot be hardcoded by experts or from manual ingestion—if that were the case, troubleshooting guides would be enough to solve all service issues. Great generative AI for service incorporates user feedback and improves personalization with usage—allowing it to learn, adapt, and make more accurate predictions based on feedback. In short, the more you use it, the better it becomes! The models can refine their outputs by analyzing user feedback, interactions, and ratings. This allows them to provide more personalized, accurate, and relevant recommendations for any service scenario. 
  • Preserving privacy: As privacy concerns continue growing, generative AI will incorporate privacy-preserving techniques to ensure user data is protected. This includes methods such as federated learning, where models are trained locally on user devices without exposing sensitive data to centralized servers. Privacy-enhancing technologies will allow generative AI to personalize recommendations while respecting user privacy. Ultimately, service problems can still be solved without divulging sensitive details, so continuously training your AI to recognize recurring issues and the best fixes will drive the desired outcomes.


Getting the most value from your generative AI tools

AI is becoming a must-have for businesses looking to remain competitive: nearly 50% of companies say AI is their top priority for tech spending over the next year

But while many are eager to invest in AI, it is essential to note that service orgs still face a common enemy: measurement. Typically, they cannot accurately measure their service landscapes and get an accurate read of their KPIs. This results in selecting an AI solution that they assume can help them and hoping their data fits into it. 

There’s a better way to make the right AI choice the first time: reverse engineer the process.

First, determine the business outcomes that you want to achieve. For example, perhaps you want to improve KPIs like First Time Fix (FTF) Rate or Resolution Time, provide more remote fixes, reduce unnecessary dispatches, or quickly upskill incoming team members. Whatever the case, this step narrows your goals and helps eliminate AI tools that can’t solve a particular problem.

Next, think about what you need to get the solution running. For instance, consider the types of data you already have versus the kinds of data you will need to secure, as well as any stakeholders that need to be involved. 

Once you understand your needs and requirements, you can accurately envision where generative AI fits into your service organization. Aquant’s Service Co-Pilot suite uses generative AI to help service orgs solve common and complex service problems through personalization and continuous learning. Service Co-Pilot synthesizes product documentation, expert knowledge, service data, and human intelligence to provide the best solution for every service scenario—thereby shortening service lifecycles, improving CX, and increasing profit margins. Service Co-Pilot’s capabilities extend to all user types: service leaders seeking comprehensive reporting and analytics, diagnostic options for customers using self-service, call center agents providing phone support, and technicians attending to fixes in the field.

It’s worth improving your delivery strategy at every stage of the service cycle — we’ll prove it to you.

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